2015 · 229 citations · 35 references
ComputingBioinformaticsComputer ScienceDistributed SystemsParallel AlgorithmsParallel ComputingDistributed AlgorithmsDistributed ComputingGraph AlgorithmsGraph AnalyticsGraph MiningGraph TheoryDistributed AnalyticsDistributed Data MiningDistributed Graph ProcessingGraph Processing SystemsParallel Graph AlgorithmsScalable Graph AlgorithmsSemantic WebSubgraph Mining
Distributed data processing platforms such as MapReduce and Pregel have substantially simplified the design and deployment of certain classes of distributed graph analytics algorithms. However, these platforms do not represent a good match for distributed graph mining problems, as for example finding frequent subgraphs in a graph. Given an input graph, these problems require exploring a very large number of subgraphs and finding patterns that match some "interestingness" criteria desired by the user. These algorithms are very important for areas such as social networks, semantic web, and bioinformatics.
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A bridging model for parallel computation
Leslie G. Valiant · Communications of the ACM · 1990 · 3.7K citations · Full text
Grzegorz Malewicz, Matthew H. Austern, Aart J. C. Bik et al. · 2010 · 3.5K citations
Algorithm 457: finding all cliques of an undirected graph
Coen Bron, Joep Kerbosch · Communications of the ACM · 1973 · 2.5K citations · Full text
gSpan: graph-based substructure pattern mining
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